Master's Thesis

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MASTER'S THESIS

Phase-0 study of a Disaster Management Satellite Constellation with a Focus on the Indian Subcontinent

Eline Conijn 2015

Master of Science (120 credits) Space Engineering - Space Master

Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Master Thesis

Phase-0 study of a disaster management satellite constellation with a focus on the Indian subcontinent

Eline Conijn

Supervisors

Narayan Prasad Nagendra Dhruva Space Bangalore, India

Marappa KRISHNASWAMY NIU Nagercoil, India

Peter von Ballmoos Institut de Recherche en Astrophysique et Planétologie Toulouse, France

Johnny Ejemalm Lulea University of Technology Kiruna, Sweden

April 13, 2015

Opgedragen aan Oma Conijn Voor altijd in herinnering

Acknowledgment Foremost, I would like to express my sincere gratitude to my direct advisor, Narayan Prasad Nagendra for the continuous support of my master thesis and research, for his patience, motiva- tion, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my master thesis. Also I am for ever grateful to him for offering an opportunity of a life time to come to India and showing me around this amazing country. The many trips on the back of the motor cycle navigating the buzzing streets filled with life have made a lasting impression. And expressed in his own words, it was also often a humbling experience. Besides my advisor, I would like to thank the rest of my thesis supervisors: Marappa Krish- naswany, Peter von Ballmoos and Johnny Ejemalm, for their encouragement, insightful com- ments, and hard questions. My thanks are also given to the many former ISRO scientists I had the pleasure to meet and who gave me insight into the Indian space industry. Also many thanks to William B. Gail, who gave me great advice about the cloud coverage. Special thanks are given to my dear colleagues at Dhruva Space, Sai, Divya and Ferran for their advice during the thesis and the "secret missions" to buy the "boss" a birthday present or eat a chicken hamburger. I am also very grateful for my Indian host family, as well as all the extending family which I had the pleasure to get to know, including all the aunties and uncles, cousins, nephews and nieces, in-laws and grandparents. They made me feel welcome, made certain that I was well taken care off, made me try new cuisine, made me part of their world including the many rituals and made me laugh. I pay tribute to all my fellow SpaceMasters, for making the two year adventure memorable, interesting and amazing. We were not just fellow students and friends, we became part of a special family. Lastly but not least, I would like to thank my parents Gijs and Adri Conijn-Meiborg and my brother Arnoud. Even when they were worried when I was so far away in countries slightly or very different from the Netherlands they were always supportive and encouraging.

Résumé This report explores the feasibility of a small satellite constellation used for disaster management in India. It shows that a small satellite constellation for the Indian subcontinent is not feasible based on the requirements and constraints set in this report and thus not worth to pursue in this form. Although it has been made clear that effective disaster management is a must, especially in India and that remote sensing from space is an excellent tool for this purpose, based on the spatial and temporal requirements derived from the occurrence and impact of the disasters, it would be impossible to propose a mission within the constraints set by this report. After a careful analysis of the Indian space budget, existing missions and the economical impact of the disasters, it is determined that a disaster management mission for the Indian subcontinent has a maximum mission budget of 30 million USD, a mass constraint of 500 kg and a volume constraint of 5 m3 for all the satellites in the constellation combined. The two instrument types with proven capabilities in remote sensing disasters, microwave and passive optical instruments, have each its own reasons to be unsuitable for a small satellite constellation. Active microwave instruments, more specific SAR, are proven to be useful in detecting and monitoring disasters. However the instruments require an antenna panel that would be too large to fit on a small satellite to meet the spatial requirements or the constellation requires too many satellites and thus exceeding the budget to meet the required revisiting time. The number of satellites and size of the SAR antenna panel is determined with a developed algorithm in MATLAB. Optical instruments are not suitable for a mission dedicated to disaster management due to the cloud cover visibility constraint and limitations set by the usage of indexes. Denna rapport undersöker genomförbarheten av ett litet satellitkonstellation som används för katastrofhantering i Indien. Den visar att en liten satellitkonstellation för den indiska subkontinenten är inte möjligt utifrån de krav och begränsningar som anges i denna rapport och därmed inte värt att fortsätta i denna form. Även om det har gjorts klart att en effektiv hantering av katastrofer är ett måste, särskilt i Indien och att fjärranalys från rymden är ett utmärkt verktyg för detta ändamål, bygger på de rumsliga och tidskrav som härrör från förekomsten och effekterna av de katastrofer, skulle det vara omöjligt att föreslå ett uppdrag inom de begränsningar som framgår av denna rapport. Efter en noggrann analys av den indiska rymdbudget, befintliga uppdrag och de ekonomiska effekterna av katastrofer är det bestämt att en katastrofuppdrag för den indiska subkontinenten har en maximal budget uppdrag på 30 miljoner USD, en massa hinder på 500 kg och en volymbegränsning på 5 m3 för alla satelliter i konstellationen kombineras. De två instrumenttyper med bevisad kapacitet i fjärranalys katastrofer, mikrovågsugn och passiva optiska instrument, har vardera sina egna skäl att vara olämpliga för en liten satellitkonstellation. Aktiva mikrovågsinstrument, mer specifik SAR, har visat sig vara användbart för att upptäcka och övervaka katastrofer. Men instrumenten kräver en antenn panel som skulle vara för stor för att passa på en liten satellit för att uppfylla utrymmeskrav eller konstellationen kräver alltför många satelliter och därmed överskrider budgeten för att uppfylla kraven återbesök tiden. Antalet satelliter och storlek SAR antennpanelen bestäms med en utvecklad algoritm i MATLAB. Optiska instrument är inte lämpliga för ett uppdrag tillägnad katastrofhantering på grund av molntäcke synlighet tvång och begränsningar som fastställts av användningen av index.

Abbreviations

AC Alarm and Crisis AVHRR Advanced Very High Resolution Radiometer CACOLA Climatic Atlas of Clouds Over Land and Ocean DDI Daily Drought Index DEM Digital Elevation Model DInSAR Diﬀerential Interferometric Synthetic Aperture Radar GSD Ground Spacing Distance InSAR Interferometric Synthetic Aperture Radar ISCPP Iternational Satellite Cloud Climatology Project INR INdian Rupies IR Infra Red ISRO INdian Space Research Organization KP Knowledge and Prevention NDVI Normalized Diﬀerence VegetationIndex NIR Near Infra Red LEO Low Earth Orbit PC Post Crisis damage SAR Synthetic Aperture Radar SWIR Short Wave Infra Red TIR ThermalInfra Red UN United Nations USD United States Dollars VIS VISible

i Symbols

3 Ae eﬀective area of SAR panel m 3 ASAR area of SAR panel m awa azimuth impulse response broadening - factor awr range impulse response broadening fac- - tor c speed of light 299792458 m/s FN system noise factor for the receiver - Ga Antenna gain - K Design margin 1-3 k Boltzmann’s constant 1.3810−23J/K La length of the SAR panel m Latmos atmospheric loss factor due to the prop- - agating wave Lion loss factor due to ionosphere - Lradar microwave transmission loss factor due - to miscellaneous sources N nominal scene noise temperature 290 K NEσ0 Noise-equivalent sigma-zero - −1 Pavg average power at transceiver W (Js ) −1 Pr received power W (Js ) −1 Pt transmitted power W (Js ) R Range vector from target to antenna m Ru Unambiguous range m SNR Signal-to-Noise ratio - vx satellite speed in slong track direction m/s Wa width of the SAR panel m η eﬃciency of SAR panel 0.6 λ wavelength m ρy slant-range resolution required m σ target radar cross section m2 ω angular frequency rads−1

ii Contents

1 Introduction 1

2 Disasters and their remote sensing requirements2 2.1 Disasters in India and their impact...... 2 2.1.1 Disaster deﬁnition ...... 2 2.1.2 Disaster data...... 2 2.1.3 Indian disaster scenario ...... 3 2.1.4 Disasters, poverty and development...... 6 2.1.5 Disaster Management ...... 7 2.2 Remote sensing of disasters ...... 8

3 Small satellites 16 3.1 Deﬁnition...... 16 3.2 Cost estimation disaster monitor constellation mission for India...... 16 3.3 Constraints ...... 17 3.3.1 Mass...... 17 3.3.2 Volume ...... 17 3.3.3 Power ...... 18

4 Satellite constellations 19 4.1 Possible constellations ...... 19 4.1.1 Sun synchronous orbits ...... 19

5 Feasibility of microwave instruments for the a small satellite constellation 22 5.1 Passive microwave sensor ...... 22 5.1.1 Feasibility for small satellites ...... 22 5.2 Active microwave sensor...... 22 5.2.1 Basic principles of SAR ...... 23 5.2.2 Feasibility for small satellites ...... 28

6 Feasibility of passive optical instruments for the a small satellite constellation 34 6.1 Visibility constraints caused by clouds ...... 34 6.2 Constraints imposed by the use of indexes...... 35

7 Conclusions and recommendations 36

Bibliography 38

Appendix A 44

Appendix B 50

iii List of Figures

2.1 Economical losses due disasters...... 3 2.2 Multihazard map of India...... 5 2.3 Importance of eﬀective disaster management...... 8

4.1 Coverage entire Earth surface...... 21 4.2 Coverage India ...... 21

iv List of Tables

2.1 Disaster data based on EM-DAT data base from a period of 1983 till 2013 for the Indian subcontinent. (for Research on the Epidemiology of Disasters(2014a)).. 4 2.4 Overview of the disaster requirements maturity...... 15

4.1 Parameters obtained with developed MATLAB code and STK simulation . . . . 20

5.1 Real and theoretical values obtained with derived MATLAB code for various SAR- instruments...... 27 5.2 Results of the required antenna area, average power and required number of satellites for detecting and monitoring earthquakes...... 28 5.3 Results of the required antenna area, average power and required number of satellites for detecting and monitoring landslides...... 29 5.4 Results of the required antenna area, average power and required number of satellites for detecting and monitoring Flooding and Drought...... 30 5.5 Overview of weight, resolution, swath, antenna area of diﬀerent SAR satellites and the cost of the satellite the instrument is on board ...... 32

6.1 Monthly average cloud amount in percentage estimated for India from CACOLO (day and average) and ISCCP(average)...... 35

v Chapter 1

Introduction

During the International Workshop in Small Satellites and Sensor Technology for Disaster Man- agement on 31st of March till the 2nd of April 2014 organized by CANEUS and Lockheed Martin, it became clear that there was a great need for more (space) systems dedicated to disaster management. Often many speakers also mentioned the use of a constellation of small satellites to meet the frequent revisiting requirement for monitoring and detecting disasters. However a crit- ical study to determine if a constellation could meet all the requirements set by the disasters has been missing till now. Dhruva Space, a company based in Bangalore, India specializing in small satellites, is interested in exploring the possibility to propose an Indian space mission dedicated to disaster management to the Indian government. In order to determine if the company should invests its time in the proposal, it was determined that a phase-0 study of a disaster management satellite constellation with a focus on the Indian subcontinent was necessary. After familiar- ization with the subject, the task was refined, so that the study and this accompanying report will answer the following question: "Is a small satellite constellation covering India dedicated to disaster management a feasible option and worth to pursue?". To answer the proposed question, chapter2 discusses what the impact of the disasters is in India and the remote sensing requirements set by these disasters. Afterward a definition of small satellites and their constraints are given in chapter3. This is followed by a proposal of a possible satellite constellation in chapter4. The last two chapters give an answer to the question if microwave and optical instruments would be suitable on board of small satellites. This report will finish with a conclusion, in which the earlier mentioned question will be answered and some recommendations. Chapter 2

Disasters and their remote sensing requirements

This chapter will deal with the background of the problem, the ﬁrst part will treat the disasters in India and their impact on the Indian society. The second part of the chapter will deal with the remote sensing techniques used and needed to detect the various disasters.

2.1 Disasters in India and their impact

Before a solution for a problem can be found, the problem needs first to be assessed and under- stood. In this section the different disasters which India faces are listed as well as their impact on the India society. From this a recommendation can be made which areas of interests should be addressed to make the biggest difference for Indian society. First a definition of a disaster is given and an overview of various types of disasters.

2.1.1 Disaster deﬁnition

The Asian Disaster Risk Reduction Center defines disasters as “sudden events, which bring serious disruption to society with massive human, material and environmental losses, these losses always go beyond the capacity of the affected society to cope with its own resources.” (Center(2008)) However there are numerous definitions of a disaster, examples are given by Pidgeon and O’Leary (2000), Denis(1995) and Keller and Al-Madhari(1996). The definition used seems dependent on the discipline using the particular term. Parker and Handmer(2013) have reviewed the concept of disaster and suggests that the preferred definition of disaster is: “An unusual or natural man-made event, including an event caused by failure of technological systems, which temporarily overwhelms the response capacity of human communities, groups of individuals or natural environments and which causes massive damage, economic loss, disruption, injury and/or loss of life.” This definition will be used in this report.

2.1.2 Disaster data

Data on disaster occurrence, their effect upon human society and their financial burden on countries is unfortunately not easily and accurately available. There is not a single institution that has taken on the role of prime provider of verified data, nor is there an internationally standardized method for assessing damage for global use. This will give rise to inconsistencies, data gaps and ambiguity of terminology, which make comparisons and use of the different data sets difficult. As a result the evaluation of a disaster situation is confusing and poses severe obstacles for prevention planning and preparedness. Recognising the need for better quality data to support disaster preparedness and mitigation, the ProVention Consortium of the World Bank Disaster Management Facility, started an evaluation of the quality, accuracy and completeness of three global disaster data sets (Below and Guha-Sapir(2003)). Two of the sets, NatCat maintained by Munich Rein- surance Company and Sigma, maintained by the Swiss Reinsurance Company are not publically available. The third one, EM-DAT, maintained by the Centre for Research on the Epidemiology of Diasters, is publically available (for Research on the Epidemiology of Disasters(2014a)). In this evaluation it was concluded that all databases where maintained with scientific rigour and all furnish the world community with acceptable levels of data on disasters. The differences in the

2 databases reduces signiﬁcantly with time. Although the report state improvements, especially for EM-DAT, it is considered that EM-DAT data will give an accurate overview of the disasters India is experiencing if the analysed time period is not too large. Also EM-DAT is conceived for scientiﬁc research and development community, while Sigma and NatCat are essentially designed to serve internal commercial policy and to service their client insurance company. Therefore the use of EM-DAT data will be more suitable for this research.

2.1.3 Indian disaster scenario

India has a diverse range of natural features and these unique geo-climatic conditions make the country among the most vulnerable to natural disasters in the world. Disasters happen with a large frequency in India and while the majority of the society has adapted itself to these regular occurrences, the economic and social costs are increasing every year, as can been seen in ﬁgure 2.1.

Figure 2.1: Increasing yearly economical costs for the India society (Srivastava(2011)).

During the last thirty years the country has been hit by 434 natural disasters and 638 man-made disasters according to data collected by EM-DAT. During these disasters 142,582 deaths out of the 170,742 total death count were caused by natural disasters. Natural disasters effected more than 1,400 million people, while man-made disasters affected around 600 thousand persons. The economic damage is estimated for natural disasters to be more than 50,000 million USD and for man-made disasters 700 million USD. As the number of people afflicted is many times higher than that of man-made disasters the focus of this report will be on the detection and monitoring of natural disasters. A complete overview of the disasters and their impact during the last thirty years can be found in table 2.1.

3 Disaster sub-group Disaster type Occurrence Deaths Injured Affected Homeless Total affected Total damage (x1000 USD) Biological Epidemic 52 14266 0 397806 0 397806 0 Biological Insect infestation 1 0 0 0 0 0 0 Climatological Drought 7 320 0 751175000 0 751175000 2041122 Climatological Extreme temperature 39 11617 250 0 0 250 544000 Climatological Wildfire 2 6 0 0 0 0 2000 Complex Disasters Complex Disasters 1 0 0 710000 0 710000 0 Geophysical Earthquake (seismic activity) 15 49620 217827 26123179 2160700 28501706 5106900 Geophysical Mass movement dry 2 61 0 0 0 0 0 Hydrological Flood 193 42299 771 610156745 8648000 618805516 34168629 Hydrological Mass movement wet 33 2667 519 217200 3616285 3834004 54500 Meteorological Storm 89 21726 15774 48885201 2181345 51082320 9661484 Technological Industrial Accident 72 4781 103961 429553 0 533514 698900 Technological Miscellaneous accident 106 5013 6639 42090 12250 60979 0 Technological Transport Accident 460 18366 9976 507 0 10483 38000

Total 1072 170742 355717 1438137281 16618580 1455111578 52315535 Natural 434 142582 235141 1437665131 16606330 1454506602 51578635 man made 638 28160 120576 472150 12250 604976 736900

4 Table 2.1: Disaster data based on EM-DAT data base from a period of 1983 till 2013 for the Indian subcontinent. (for Research on the Epidemiology of Disasters(2014a)) As can be seen in 2.1 there are diﬀerent disaster groups and types, the complete overview of the deﬁnitions for these disaster groups and types taken from the EM-DAT website can be found on for Research on the Epidemiology of Disasters(2014b).

Natural disaster vulnerability proﬁle in India

As can be seen from the 2.1, India is highly vulnerable to floods, droughts, cyclones, earthquakes and landslides. Out of 35 states and union territories in the country, 27 of them are disaster prone. About 60 per cent of the landmass is vulnerable to seismic occurrences of moderate to very high intensity. Twelve percent of land, corresponding to 40 million hectares is prone to floods and river erosion, and the average area affected by floods annually is about 8 million hectares. Approximately 5700 kilometres out of 7516 kilometres long coastline is prone to cyclones and tsunamis, and 68 percent of the cultivable area is susceptible to drought (Srivastava(2011)). A multi-hazard map of india can be seen in 2.2.

Figure 2.2: The diﬀerent disasters zones are shown over a map of India based on the data and information compiled the Ministry of Urban Developement and Poverity Alleviation, India Srivastava(2011).

According to 2.2, there are five distinctive regions of the country concerning vulnerability to disaster, i.e. the Himalayan region, the alluvial plains, the hilly part of the peninsula, and the coastal zone, each having their own specific problems. The plain is affected by floods almost every year, while the Himalayan region is vulnerable to disasters like landslides and earthquakes. The desert part of the country is affected by droughts and famine, while the coastal zone is susceptible to cyclones, storms and tsunamis. The basic reason for this increased vulnerability is the natural geological setting of the country. For example the geo-tectonic features of the Himalayan region and the neighboring alluvial plains make the region susceptible to earthquakes, land slides and water erosion.

5 The western part of the country, including Rajasthan, Gujarat and some parts of Maharashtra are hit very frequently by drought. Other parts of the country are also facing drought if the Monsoon worsens. Besides the natural factors, various human induced activities like increasing demographic pres- sure, deteriorating environmental conditions, deforestation, faulty agricultural practices and grazing, construction of large dams on river channels etc. accelerate and increase the frequency of the disasters in India.

Consequences of climate change

Climate change is expected to increase the frequency and intensity of the current extreme weather events and will cause new challenges of different spatial and social-economic impacts on communities. The climate change is expected to have severe impacts on the hydrological cycle, water resource, droughts, floods, drinking water and other related areas. The impact would be particularly disastrous for developing countries, including India and would be most taxing for poor vulnerable communities, which make up one quarter and one half of the population of most In- dian cities. Already there is clear evidence that the climate change is influencing India, extreme rainfall has substantially increased over large areas, particularly over the west coast and west central India (Srivastava(2011)).

2.1.4 Disasters, poverty and development

Research and practice support the theory that a strong correlation between disasters and poverty exists. It is well documented (Skoufias(2003), Rubonis and Bickman(1991), Noy(2009), Kahn (2005), Wisner and Luce(1993)) that the developing countries including India repeatedly subject to disasters experience stagnant or even negative rates of development over time. Although every disasters has unique consequences, an general overview of the ways in which disasters harm poor countries beyond the initial death, injury or destruction are discussed next. • National and international development efforts are stunted, erased or even reversed. • Sizeable portions of GDP must be diverted from development projects, social programs or dept repayment in order to manage the disaster consequences and start recovery efforts. • Vital infrastructure is damaged or destroyed, requiring years to rebuild. • Schools are damaged or destroyed, so that students are without adequate education for months or even years. • Hospitals and clinics are damaged or destroyed, resulting in higher levels in vulnerability to disease of the affected population. • Formal and informal business are destroyed, giving rising to unemployement and economic instability and strength. • Desperation and poverty leads to a rapid upsurge in crime and insecurity. • People are forced to leave the affected area, often to never return, thereby extracting in- stitutional knowledge, cultural and social identity and economic viability from areas that could not afford to spare such resources.

6 2.1.5 Disaster Management

The human response to the earlier mentioned disasters is coordinated by various groups dealing with disaster management. Modern disaster management is based upon four distinct components, mitigation, preparedness, response and recovery (Coppola(2006)). • Mitigation involves the reducing or eliminating of the likelihood or the consequences of a disasters or both, so that the hazard impacts society to a lesser degree. • Preparedness is equipping people who might be impacted by a hazard or who may be able to help those impacted with the tools to increase their chance of survival and to minimize their financial and other losses. • Response involves taking action to reduce or eliminate the impact of disasters that have happened or are happening, to minimize further suffering, financial loss, or a combination of both. • Recovery has as goal to return victims’ lives back to a normal state after the impact of the hazards consequences. This phase normally begins after the immediate response has ended. In reference to Tobias et al.(2000) disaster managements is composed out of three components, Knowledge and Prevention (KP), Alarm and Crisis (AC), and Post-Crisis damage assessment (PC). Although different names, these components (Mitigation and Preparedness are taken together) are very similar as the definition given by Coppola(2006).

Importance of disaster management and remote sensing

According to the Overseas Development Institute and the United Nations(UN) the economic losses from disasters have topped one trillion USD dollars worldwide since 2000, growing at a faster rate than GDP per capita in OECD countries in the same period. Despite these escalating losses only five percent of the humanitarian fiance is spent on reducing the risk of disasters. Without a major increase in investment to reduce current and future risks, spending on relief and reconstructing is likely to be come unsustainable (Mitchell and Wilkinson(2012)). Secondly it is proven that effective disaster management reduces the losses a society is suffering as well as enabling a faster recovering as is shown in figure 2.3. An import tool to help disaster policy makers make better decisions before, during and after the occurrence of a disaster is remote sensing from space applications. Satellites are able to view remote areas as well as to screen large areas at the same time at regular time intervals. Furthermore in Tobias et al.(2000) it is stated that the maturity of the applications, technology and users and service are developed enough to be able to be a viable tool for disaster management.

7 Figure 2.3: The graph shows the difference in recovering between effective disaster management versus ineffective disaster management.(Coppola(2006))

2.2 Remote sensing of disasters

In this chapter an overview will be given of the different requirements to be able to monitor and detect the different disasters For each disaster the parameters that needs to be measured, what type of sensor is necessary to measure the parameters, the required sensitivity of the sensor and the required revisiting time are listed. The disasters that are discussed are earthquakes, landslides, flooding, wildfires and drought. As storm and other weather related events are already extensively monitored by the various weather satellites, storm and extreme temperature are excluded from the list. The complete overview of the derived requirements for each phase (KP, AC, PC) can be found in table 2.4.

8 Disaster Earthquake

Sensor technique Displacement de- InSAR/DInSAR Gravitational Optical TIR termination with ﬁeld measure- GPS ment

Short description GPS geodetic network multitemporal radar observations; Measuring the manual interpreta- sensors in the infrared in the risk areas ratio or difference between multi- gravity potential tion of topograph- spectrum monitoring temporal images and then apply- signature of an ical changes from Earth’s thermal field ing supervised or unsupervised clas- earthquake; optical imagery sification/phase difference of two or both in colour and more SAR images are used to calcu- panchromatic lated the differences in range Disaster parameter ground deformation small-scale features in the defor- gravity signature topographical change in the Earth’s measured mation field associated with earth- mainly associated changes surface temperature quakes; ground changes and infras- with the vertical and near-surface tructure damages displacement atmosphere layers 9 "Disaster Phase" KP, AC KP, PC AC, PC PC KP Required sensitivity <1cm vertical height AC<4-5cm; KP<1mm (horizontal <0.2-1mm of the <2 °C difference deformation) vertical deformation: geoid close at rupture=1m, rest required ground reso- not relevant KP, PC<5m, 1m ideal ? KP, PC<5m, 1m <10 km lution ideal Required revisiting permanent coverage KP:12 hours, PC<2 days, <3 hours AC <1 hour KP:12 hours, <5 days time ideal PC<2 days, <3 hours ideal optimal band/fre- not relevant L-band preferred, C- and X-band - Visible mid-IR (8-8.5 µm) quency also possible to generate DInSAR; L-band: lower resolution, but greater range of surface cover types; C-band: high resolution,only provide reliable interferograms for coherent, non-vegetated surfaces. limitations economic constraints InSAR: variability of backscatter of Only very large Can be subjective, only suitable for large limit the widespread different region, lack of quantitative earthquakes time-consuming for earthquake M>4.5, global deployment estimations and dependence on in- (>M8) could widespread events, "normal" model neces- of these networks; cidence angle; both methods: only be measured. and nonrepeatable. sary. The usefulness of discrete samples possible to measure ground defor- (GRACE). The Only possible to be the technique is highly mation of a moderate earthquake usefulness of tech- used during cloud- debated (>M5); displacements close to fault, nique is highly less day too large, interferometric phase can- debated not be reconstructed due to ambiguity in phase unwrapping. Dependent on spatial baseline and DEM accuracy Instrument, Applica- A A B and C A B and D tion, Processing Matu- rity Sensor examples GPS satellites ERS 1/2, Cosmo-SkyMed GOCE, GRACE IKONOS, Quick- MODIS (Terra, Aqua) bird 10 reference Tralli et al.(2005) Zhou et al.(2010), Sansosti et al. Mikhailov et al. Tronin(2006), Ouzounov and Freund (2014), Stramondo et al.(2011), (2014),Akhoondzadeh Wood and all (2004), Tronin(2006) Wood and all(2002) et al.(2011), De Vi- (2002) ron et al.(2008) Disaster Landslide Flooding

Sensor technique SAR Optical Optical SAR/InSAR

Short description two method manually/visible 3 methods: 1.) manually/vis- Imagery in the visible and Distinct backscatter between interpretation and multitem- ible interpretation, 2.) multi- NIR, can be used to cre- land and water, a threshold poral image analysis with im- temporal image analysis with ate the Normalized Difference technique is applied to segre- ages before and after event images before and after event Vegetation Index, stereo data gate flooded regions in Active 3.) change in DEM, can be used to derive DEM’s Microwave images. InSAR is used to compare backscatter of regions over different times. Stereo imagery can be used to derive DEM’s Disaster parameter no distinct backscatter signa- no distinct backscatter signa- NIR: absence of light due Backscatter of water measured ture, indirect measurement of ture, indirect measurement of to strong absorption of wa- changes in the landscape changes in the landscape ter; VIS: used to differentiate

11 between various other object absorbing light "Disaster Phase" (KP), AC, PC KP, AC, PC KP, AC, PC (KP), AC, PC Required sensitivity preferred incidence angle 40 vertical: 0.5m DEM (vertical): 1-3m, 0.10- DEM (vertical): 1-3m, 0.10- degrees to 59 degrees, stable 0.15m ideal 0.15 m ideal orbit, variations not exceeding +/- 1km required ground reso- KP<3m, 0.5m ideal; KP<3m, 0.5m ideal; KP: land use: 30m, 4-5m AC<30m, <5m ideal; PC lution AC/PC<10m; <3m ideal AC/PC<10m; <3m ideal ideal, infrastructure: 5m, damage assessment: 2-5m, <1m ideal; AC<30m, <5m 0.3m ideal ideal; PC: damage assessment: 2-5m, 0.3m ideal, land use: 30m, 4-5m ideal Required revisiting KP/PC: 1 day-2 days; AC<1 KP/PC: 1 day-2 days; AC<1 KP: 1-3 yrs, 6 months ideal, AC<1 day, <3hours ideal ; time hour hour vegetation: 3 months, 1 PC: 2-3 days/<1 day month ideal; AC<1 day, <3 hours ideal <1 day; PC: 2-3 days/<1 day optimal band/fre- L-band: lower resolution, but panchromatic , multi- and Near infrared (0.8-1.1 µm) L-band (C-band possible, but quency greater range of surface cover hyper-spectral sensors in the in combination with another not preferred) types; C-band: high resolu- visible, near and short wave Near/Mid Infrared (2.08-2.35 tion, t only provide reliable infrared µm), for NDVI and DEM: interferometers for coherent, PAN/MSI non-vegetated surfaces. limitations Can be subjective, time- Can be subjective, time- Cloud limits severely the Determining the threshold is consuming for widespread consuming for widespread view, during monsoon 60- not straight forward, many events, and non repeatable. events, and non repeatable. 80% cloud coverage factors, making determining Slopes gives an interferomet- Clouds limits view the threshold for each situa- ric analysis tion diﬀerent. Instrument, Applica- AAAA tion, Processing Matu- rity Sensor examples ERS IKONOS, Quickbird ASTER, Quickbird, MODIS RADARSAT 1,2 reference Wood and all(2002),Singhroy Wood and all(2002) Wood and all(2002), Wood and all

12 and Molch(2004),Hervás ,Cheng et al.(2004),Zhou Smith(1997),Wang et al. (2002),Townsend(2001); et al.(2003); Nagarajan et al.(2002), Joyce et al. (2002),Wiesnet et al.(1974); Brivio et al.(2002),Martinis et al.(1998) Pairman et al. (2009),Nichol and Wong ISLAM and Sado(2000); et al.(2009),Nico et al. (1997),Metternicht et al. (2005),Oštir et al.(2003); Sheng et al.(2001); Jain (2000),Sanyal and Lu(2004) (2005),Massonnet et al. Singhroy and Molch(2004); et al.(2006),Barton and (1993),Colesanti and Wa- Casson et al.(2005); Tsut- Bathols(1989),Hastings and sowski(2006) sui et al.(2007),HUANG Emery(1992),Kogan(1997) and CHEN(1991); Cheng et al.(2004); Zhou et al. (2002),Hervás et al.(2003); Nagarajan et al.(1998) Disaster (Wild)Fire Drought

Sensor technique Optical TIR SAR Optical Thermal In- Passive mi- frared crowave/SAR

Short description Imagery in the sensors in the in- Backscatter inten- Vegetation indices visible and NIR, frared spectrum sity changes for derived from diﬀer- 3 methods: 1.) monitoring tem- both changes in ent channels in the manually/visible perature elevation water in vegetation optical spectrum interpretation, caused by (wild) and burnt areas. can be used to de- 2.) multitemporal ﬁres Active microwave tect changes in leaf image analysis signal can be used chlorophyll, mois- with images before to measure this ture content and and after event 3.) backscatter thermal conditions. change in DEM Disaster parameter KP: fuel mapping: KP: Risk ar- KP: fuel mapping: indirect mea- indirect mea- soil moisture

13 measured vegetation struc- eas: vegetation vegetation struc- surement of leaf surement of soil ture, biomass; AC: stress; AC: ele- ture, biomass; risk chlorophyll, mois- moisture by mea- ﬁre, smoke, PC: vated temperatures areas; PC; charac- ture content and suring land surface burnt areas based associated with ﬁre teristic backscatter thermal conditions. temperature on multi-temporal burnt areas "Disaster Phase" KP, AC, PC KP, AC, PC KP, PC AC AC AC Required sensitivity AC: not more than AC: detection <2 degrees, accuracy better 5% false alerts range at least 700 <50W m2 than 5%v/v, ideal K, not more than 3% v/v, incidence 5% false alerts angle preferred between 35 and 50 degrees required ground reso- KP: 5-30m; KP: 5-30m; AC KP: 5-30 m; PC <1km, <100m <100 m <50km lution AC<250m (de- <250m (detection), <30m, <5m ideal, ideal tection), 1 1m(mapping); <5m m(mapping); PC<30m, <5 m PC<30m, <5m ideal ideal Required revisiting KP:<16 days, KP:<16 days, KP:<16 days, <7 days, ideal <3 <7 days <7 days time ideal: 1-2 days; ideal: 1-2 days; ideal: 1-2 days; days AC: <30 mins, AC: <30 mins, PC: 1 day ideal: <15 mins ideal: <15 mins; (mapping), < 5 PC: 1 day mins (detection); PC: 1 day optimal band/fre- KP: multispec- KP: multispec- L-band: lower multi-spectral with 8-14 µm L-band quency tral(VIS) including tral(VIS) including resolution, but at least bands: 0.4- NIR(0.9 µm), NIR(0.9 µm), greater range of 0.7 µm and 0.7-1.1 SWIR(1.6 µm) SWIR(1.6 µm); surface cover types; µm AC: SWIR (1.6 C-band: high reso- µm) MIR (3 to lution, only provide 4 µm), desirable: reliable interfero- HIR (11 µm) grams for coherent, non-vegetated surfaces.

14 limitations Visibility severely Visibility limited index based, cloud index based limited by cloud by cloud cover, cover limits unob- cover and smoke however penetra- structed view tion if light smoke and haze possible Instrument, Applica- AAAABD tion, Processing Matu- rity Sensor examples MODIS, LAND- AVHRR, MODIS ERS, JERS AVHRR Landsat 5-7 SMOS SAT, SPOT, Ikonos reference Wood and all Wood and all Wood and all Kogan(1997),Kni- Anderson and Kus- Entekhabi et al. (2002),Joyce (2002),Dozier (2002),Giglio pling(1970); Wool- tas(2008); Ander- (2010)Walker and et al.(2009),Roy (1981),Giglio and and Kendall ley(1971); Jacque- son et al.(2011); Houser(2004) and Landmann Kendall(2001) (2001),Cou- moud and Baret Gillies and Carlson (2005),Trigg et al. turier et al. (1990) (1995) (2005) (2001),Bourgeau- Chavez et al. (2002); Siegert et al.(2001); Tanase et al.(2010)

Table 2.4: Overview of the disaster requirements maturity

A: Clearly demonstrated to work using standard image processing systems and is openly available in the literature; B:Shown to work with experimental image data sets or over limited areas with very small pixels or over global scales with large pixels; C: If extent is bigger than several pixels; D: Not

15 widely available in literature but theoretically should be a potential use; E: Not feasible Chapter 3

Small satellites

In this chapter a deﬁnition of a small satellite, as well as the constraints of a small satellite will be derived.

3.1 Deﬁnition

Unfortunately there is quite some confusion about the definition of small satellites as is described in Sandau(2010). The IAA (International Academy of Astronautics) makes the suggestion that all small satellites below 1000 kg could be classified as small satellites (Sweeting(1996)). The problem with the previous definitions, is that all definitions are solely based on the weight of the satellites. Although there is a strong connection between the weight and the costs in general, due to miniaturization of the instruments and equipment, light weighted satellites can cost as much as heavier satellites. An example of this is the TecSAR mission, a spaceborne radar mini satellite technology demonstration mission of Israel’s Minister of Defense (Kramer(2014a)). It is one of the smallest satellites with a SAR on board and weighting only 300 kg, therefore classifying it as a small satellite according to the conventional definitions. However the cost estimation is approximately 200 million USD (Def(2014)), which is higher than satellites with that weight would normally cost (Kramer(2014a)). Furthermore most missions are driven by cost, therefore it proposed to define small satellites in terms of cost.

3.2 Cost estimation disaster monitor constellation mission for India

To determine a reasonable estimation of the maximum cost of a small satellite disaster constellation for India, a closer look is taken into the Indian budget of space, the cost of an existing disaster monitoring constellation, the DCM3 and the cost of the disasters. According to the outcome budget of the year 2013-2014 prepared by the department of space of the government of India (DOS(2014)) the total budget was estimated for that year on INR 67,920.4 million (approximately 835 million euros and 1.12 milliard USD). In the same document the cost estimated for SARAL, a satellite with Argos and Altika on board with an approximate weight of 400 kg, was 737.5 million INR (approximately 9.1 million euros and 12 million USD). This is not including the costs for the two payloads, which are carried by CNES, France and the launch. Its expected life time will five years (Kramer(2014b)). A mission dedicated to the continuous observation of the Indian subcontinent for quick monitoring of disasters, natural calamities and episodic events, GISAT will cost INR 3,920 million (48.4 million euros, 64.47 million USD), excluding the launch costs and operation costs. GISAT is a geosynchronous satellite capable of imaging in visible and thermal band with 50 meters resolution (DOS(2014)). Lastly ResourceSAT-2A, providing continuity of data in the area of natural resources management, costs 2,000 million INR (24.7 million euros, 32.9 million USD). It should be noted that this mission has a strong heritage of previous missions lowering the costs. The weight of the satellite is approximately 1200 kg and its mission duration is five years (Kramer(2014c)). An existing small satellite constellation dedicated to disaster monitoring has been developed by Surrey Satellite Technology Ltd. The first constellation was simply called Disaster Monitor Constellation, consisting out of 5 satellites, each weighting around 100 kg (da Silva Curiel et al.

16 (2002)). The cost are claimed to be less than 50 million USD for the entire network (Ward et al.(1999)). The newest constellation developed for this purpose is the DMC3 constellation, consisting out of three SSTL-300S1 satellite platforms, weighting 350 kg each. It carries a high optical instrument with a 0.75m-1m GSD in PAN and 4m in VIS and NIR. The cost of the development, launch and insure these platforms are estimated at 110 million pounds (137 million euros, 184.8 million USD). The spacecraft design life is 7 years (Kramer(2014d)). Lastly the damage caused by the different disasters is taken into account. It is assumed that the satellite mission should not exceed the damage cost of the natural disasters. Although it should be noted that the natural disasters also cause many casualties, which can never be expressed in currency. Based the last column of table 2.1 it is concluded that a mission detecting solely wild fires in India is most likely not cost effective. As most natural disaster can not be prevented, it is assumed that it is unlikely that a future mission would be able to reduce the cost with for example 50 percent and for the remaining disasters a reduction of 10 percent is taken as limit to be economical feasible. The cost of a mission for the detection and monitoring of landslides can not exceed 0.5-1 million USD for a two year mission and 1-3 million USD for a five year mission to be cost efficient. The cost for a space mission for the detection and monitoring of drought, landslides and earthquakes could be exceeding 10 million USD for a two year mission and 30 million dollars for a five year mission to be cost effective. However based on the Indian budget, the existing constellations and the idea to develop a low cost small satellite constellation it has been decided that the mission should not cost more than 30 million USD for a five year mission.

3.3 Constraints

This section will give an overview of the some major constraints imposed by the cost limitation.

3.3.1 Mass

Launching cost for small satellites are normally estimated to be around 20 percent of the total budget (Sarsﬁeld(1998)) or even as high as 25 percent-70 percent (Koelle and Janovsky(2007)). Although the speciﬁc transportation costs, the cost per kg payload depends strongly on payload size and launch frequency, it is normally estimated between 4,000-15,000 USD per kilogram for a LEO orbit (Fut(2002)). However after speaking with several former ISRO scientists and comparing prices at Spa(2014) the launch costs for small satellites (<300 kg) are higher per kg. An estimation was given that the cost per kg on board of the Indian launcher PLSV is around 15,000 dollars, meaning that the total combined weight of the satellites in the constellation could be around 500 kg assuming that the cost of the launch is 25 percent of the budget.

3.3.2 Volume

Small satellites are often secondary or even tertiary payload, meaning that priorities are given to the primary payload of a launch vehicle, resulting in volume constraints. There does not appear to be an universal consensus for the the maximum volume for small satellites, but based on Spa (2014) and own estimations the total maximum volume of the entire constellation together is around 5m3.

17 3.3.3 Power

Again similar as for the volume constraint, there does not appear to be an universal consensus for the the maximum available (average) power. However in da Silva Curiel(2003) the available average power for a 500 kg satellite is estimated as 400 W , for a 250 kilogram satellite around 150 W and for a 100 kg satellite, between 50 and 70 W .

18 Chapter 4

Satellite constellations

In this chapter a constellation for small satellites is proposed, as well as a method to determine the number of satellites necessary to meet the requirements is shown

4.1 Possible constellations

A principal characteristic of any satellite constellation is the number of orbit planes in which the satellites fly. It is advantageous to place more satellites in a smaller number of planes, as moving between planes uses more propellant than moving them within a plane. This provides a benefit when a satellite fails or a new satellite is added to a given orbit plane and the remaining satellites needs to be rephased which require only little propellant. Furthermore satellite constellations only consisting of one or two orbit planes can be more responsive to changing user needs than a system with multiple planes can. Especially if the revisiting time might change, it is easier to launch less or more satellites and simply rephase the satellites in one orbit plane (Wertz and Larson(1999)). Especially for small satellites with their strict mass constraint this is a benefit, therefore a constellation is proposed with as less orbit planes as possible.

4.1.1 Sun synchronous orbits

A sun-synchronous orbit is an orbit that maintains a constant angle of its orbit plane relatively to solar illumination over the entire year. It is therefore excellent for satellites hosting optical instruments, choosing their phase of the sun angle to favor illumination of the surface (Fortescue et al.(2011)). Also sun-synchronous orbits are suitable for radar satellites, in contrast with optical satellites they tend to favor illumination of the spacecraft (Merrill(2008)). Furthermore satellites in a sun-synchronous orbit make use of the natural precesses in inertial space caused by the oblateness of the Earth, and requiring less in-orbit maneuvering to stay in their orbit (Sidi(1997)). Thus for a small satellite constellation with optical or radar instruments based a sun-synchronous orbit is advisable. The method to determine the parameters of this constellation are explained in the next section.

Determination of the number of satellites and spacing of the satellites in the sun-synchronous orbit

Depending on the height of the orbit, the number of rotations a satellite in a sun synchronous orbit makes during one day is in between 13 and 16 for LEO. This results, again depending on the height of the orbit, in a certain spacing between the ascending (and descending) nodes. The longitude difference at the node crossing for a new day from the initial node at the first day can change for each new day. The difference is determined by the repeat time, the number of days the satellites require to fly over exactly the first or initial node. By placing multiple satellites in a sun-synchronous orbit by a pre-determined on-orbit spacing it is possible to cover the longitudes where the first satellite has not been flying over. To determine the number and the phasing of the satellites in orbit a MATLAB code is generated with as input the height of the orbit and the required revisit frequency. The orbital parameters for a sun synchronous orbit are determined using the method described in Boain(2004). An iteration is used to generate

19 all the the longitudes corresponding to the ascending nodes in case for optical instruments for the required revisiting time. For SAR instruments all the the longitudes corresponding to the ascending nodes and descending nodes are taken into consideration as SAR instruments could also be operated during night time. Afterward the largest distance between the longitudes is found, this distance is divided by the swath length to determine the additional required satellites. Lastly the spacing of the satellites is determined by comparing the rotating time of Earth and the orbital period of the time. The full MATLAB code can be found in the appendix.

Validation of the developed method

To validate the results obtained with the developed MATLAB script, the obtained parameters for a radar instrument scenario, as stated in table 4.1 are used as input in the System Tool Kit (STK) of AGI, a software modeling environment used to model complex systems, such as aircraft, missiles, satellites and their sensors (AGI(2014)). The resulting 2D graph, seen in ﬁgure 4.1, shows the laid down ground track of the four diﬀerent satellites. As can be seen in the graph most of the Earth surface has been covered with the sun synchronous orbit constellation. After a careful analysis it is however observed that there are some small spots on the Earth surface that are not completely covered, as can be seen in for example the north of India in 4.2. Considering that most radar instruments have a variable looking angle it seems an acceptable assumption that the derived number of satellites should be able to provide the needed coverage in the required revisit time.

Microwave band L Parameters MATLAB STK Along-track resolution (meters) 25 - Revisiting time 2 days 2 days Orbit height (kilometers) 785 785 radar frequency (GHz) 1.275 - Incidence angle 24.05 0 Repeat cycle days 253 - Period (min) 100.56 100.5575 Orbit Inclination (degrees) 98.539 98.4678 Swath(km) 524.6 524.6 Minimum number of satellites 2 - Number of satellites sun synchronous one or more orbit planes 4 4 In-orbit spacing(degrees) 48.94 48.94

Table 4.1: Parameters obtained with developed MATLAB code and STK simulation

20 Figure 4.1: Simulation of a constellation of four sun-synchronous satellites showing the resulting swath coverage.

Figure 4.2: Simulation of a constellation of four sun-synchronous satellites showing the resulting swath coverage over India.

21 Chapter 5

Feasibility of microwave instruments for the a small satellite constellation

In this chapter, an short overview will be given about the speciﬁcs of both the active as well as passive microwave instruments and a conclusion will be drawn about the possibility of a passive or active microwave instrument on board of small satellites that will satisfy the requirements set for disaster management.

5.1 Passive microwave sensor

During microwave remote sensing the thermal radiation or brightness temperature of a target is measured, which is determined by the physical temperature and emissivity of the target. Although it would be possible to measure soil moisture in diﬀerent bands in the microwave spectrum, from the requirements is was clear that the L-band would be most suitable to monitor drought.

5.1.1 Feasibility for small satellites

The long wavelength of the L-band frequencies implies the need for large antennae to achieve useful spatial resolution. For example, using classical solutions on LEO satellites while obtaining a ground resolution of 50 km or less results in an antenna size of up to 20 meters (Monerris and Schmugge(2009)). However new techniques have been developed to face this problem, one of them is ﬂying on a space mission to measure soil moisture: ESA’s SMOS. SMOS has a a Y-shaped antenna array with three arms, each arm is approximately 4.5 m and has 21 dual-polarization L-band antennae spaced 0.875 times the wavelength. The ﬁeld-of-view is a hexagonal-like area of less than 1000 kilometers. SMOS aims at providing global maps of soil moisture every three days with a ground resolution better than 50 kilometer. The total mass of the satellites is 658 kg with the payload weighting 355 kg. Besides that it is clear that the weight and payload size make this technique not suitable for small satellites, as passive microwave technology is still recently developed, the development and implementation cost will be high. The cost for the SMOS program is estimated around 315m euros(465m USD) (Amos(2009)). Therefore passive microwave instruments can not be used in a small satellite disaster monitoring constellation.

5.2 Active microwave sensor

Although there are different types of active microwave instruments in space, as can been seen in chapter 18 of Merrill(2008), the focus will be on SAR. SAR is a combination of radar hardware, waveforms, signal processing and relative motion that creates imagery like renderings of stationary targets and scenes of interest. The principal product of any basic SAR application is a fine- resolution two-dimensional intensity image of the illuminated scene. As seen during the analysis of the different detection methods for the various disasters, SAR instruments have many applications in the field of disaster monitoring. In this chapter, an short overview will be given about the specifics of SAR instruments and a conclusion will be drawn about the possibility of a SAR instrument on board of small satellites.

22 5.2.1 Basic principles of SAR

To avoid misunderstandings it is necessary to discuss some terminology. Range resolutions or downrange resolution is used to express the resolution along the line-of-sight (LOS) from the radar to the target region and cross range resolution or azimuth resolution to express the resolution along the direction perpendicular to the LOS and the parallel to the ground. Sometimes range resolution is called cross-track resolution if the LOS remains perpendicular to the direction of flight and crossrange resolution is then called along-track resolution (Merrill(2008)). Synthetic aperture radar works similar as a real aperture radar. Both send microwave pulses to the earth surface, which are scattered back to the receiver. However to improve the linear cross range resolution, an aperture of modest size may be moved along a path in space if using appropriate coherent processing and so creating a synthetic aperture. The SAR instrument in consideration is the focused SAR, indicating that the phase information has been optimally processed to produce resolution comparable to the theoretical limit. There are different types of focused SAR, an overview can be found in chapter 17 in Merrill(2008). For the monitoring of large areas with a fine crossrange resolution squinted stripmap SAR is preferred according to the derived requirements in 2.2. The squinted stripmap SAR technique is similiar to the stripmap SAR, where a beam, normal to the flight path, continously observes a swath or strip of terrain parallel to the flight. However for squinted stripmap SAR, the antenna boresight is not normal to the flight path (Merrill(2008)). To determine if a squinted stripmap SAR could be fitted on board of a small satellite, a MATLAB script is developed to estimate the required seize of the SAR panel as well as the required power to meet the disaster monitoring requirements. First some highlights of the developed method will be shown in 5.2.1, afterward the method will be verified and validated with existing SAR missions.

Squinted Stripmap SAR seizing

The developed MATLAB method seizing the SAR instrument consist out of three parts, the ﬁrst part is the determination of the required area of the panel. The second part is determining the required average power. The last part is determining the necessary number of satellites, based on the method described in chapter4 to meet the temporal resolution. The script prompt the user to give the required (along track) resolution, the required revisiting time, the desired orbit height, the radar frequency and lastly the minimum and maximum incidence angle. The incidence angle is deﬁned as the angle between the bore sight and the normal with the ground plane, it is complementary to the grazing angle.

Required area SAR panel Based on Merrill(2008) and Freeman et al.(2000), the minimum surface of the radar panels can be estimated if the requirement is to achieve the best possible resolution. The equation to calculate the minimum area is given by equation 5.1

4vxλR Aa = WaLa > tan η (5.1) c The range R and the incidence angle η are calculated according to the pulses in the air principle that can be found in appendix B of Doerry(2006). However Merrill(2008) points out that for the unambiguous range the following conditions holds up as stated in equation 5.2.

Ru c ≤ (5.2) ρ(y) 4.7vx

23 The unambiguous range corresponds to the distance between the forward edge and the far edge of the region to be mapped. In Freeman et al.(2000) the relationship between the unambiguous range and the crossrange resolution is given by equation 5.3.

Ru c ≤ (5.3) ρ(y) 2vx

The reason for the discrepancy is that Merrill(2008) takes into account that the equation 5.2 is only valid for idealized patterns. The real unambiguous range must be smaller to allow for the fact that the antenna pattern are not zero outside the mapping area. Lastly in Freeman et al. (2000) it is mentioned that SAR designers often introduce an additional design margin with a value in between 1 and 3. In the end the area of a SAR panel can be calculated by equation 5.4;

K9.4vxλR ASAR = tan η (5.4) c

Required average power The starting point in the determination of the required average power of the SAR instrument is the radar equation given by equation 5.5

PtGaAeσ Pr = 2 4 (5.5) (4π) R LradarLatmos

From this equation an expression can be found for the Signal-to-Noise ratio as can be seen in 5.6, the full derivation can be found in Doerry(2006).

P G 2λ3ρ σ 1 a a f n SNR = avg A y 0,ref wr wa 2 3 (5.6) 2 (4π) R vx (kT FN ) awr LradarLatmos LrLa fref

The expressions in the square brackets are typically nearly unity and therefore often ignored. In deviation from Doerry(2006) the losses for the SAR are simulated as 2.5 dB for radar losses. The atmospheric losses are roughly estimated for a rainrate of 12.5 mm/hr and a propagation scenario with a rainrate cell height of 5 km, more details can be found in Danklmayer et al. (2009). In the same publication it is stated that the ionospheric loss eﬀects should only be taken into consideration for low frequencies, namely the P- and the L-band. Therefore the loss for the L-band is estimated for the Indian region from ITU-R(2013) as 3 dB. In Merrill(2008) the equation for the SNR is almost identical as in equation 5.6, except for the term awr, which is the azimuth impulse response broading factor. In Merrill(2008) it is also stated that an useful expression to consider is the expression for the noise-equivalent sigma-zero (NEσ0), deﬁned as the level of σ0 that produces a received power equal to the thermal noise power, a SNR of unity. Thus the SNR is set to 1 and the following equation 5.8 can be derived.

2 3 2 (4π) R vx (kT FN ) awrLradarLatmosLion NEσ0 = 2 3 (5.7) PavgGAλ ρy Rewriting this equation gives an expression to determine the average power necessary to obtain a certain along-track resolution as can be seen in equation 5.8

2 3 2 (4π) R vx (kT FN ) awrLradarLatmosLion Pavg = 2 3 (5.8) NEσ0GAλ ρy Although it is recommended that a in-depth study is necessary to exactly determine the required NEσ0, most SAR instruments are designed to have a NEσ0 in between −25dB and −20dB (Merrill(2008)).

24 Validation of the developed MATLAB script

The developed MATLAB code is validated by comparing the theoretical values found by the MATLAB script with actual values of flown satellites. Three satellite missions are compared, namely SeaSAT, the first SAR satellite mission measuring in the L-band, RISAT, the first Indian SAR satellite measuring in the C-band and TerraSAR-X, the first civilian SAR satellite mission measuring in the X-band (Merrill(2008)). Furthermore the derived parameters of a proposed mission with a X-band SAR compatible for a piggyback satellite of 100 kilogram in Saito et al. (2013) are compared with the developed MATLAB script. The results can be found in table 5.1 After comparing the theoretical values and the actual values it is seen that the the theoretical average power for SeaSat and RISAT is lower than the actual value. Especially the difference between the values of RISAT is quite large. A reason for the difference can be that the losses are not estimated correctly and are higher in real life. This is a good possibility, especially for the case of SeaSAT, which has been flown 34 years ago. The calculated values and the actual values for the more recent X-band satellites matches quite closely. Although the comparison of other satellites in the L-band and C-band to improve the accuracy of the script is advisable, the MATLAB script should give a correct estimation of the required average power, maximum swath and minimum area.

25 SAR specifics for valida- LCX tion Specifics SEASAT Theoretical RISAT (fine Theoretical TerraSAR-X Theoretical PiggyBack Theoretical resolutions (stripmap SAR stripmap mode) mode -1) Along-track resolution design: 25 25 3-6 3 3 3 3 3 (m) Orbit height (km) 785 785 536 536 514.8 514.8 618 618 radar frequency (GHz) 1.275 1.275 5.35 5.35 9.65 9.65 9.65 9.65 Incidence angle 23+/-3 24.04 12-55 (20-30) 26.5355 20-45: full 26.49 21 21.6 performance (15-60: possible) Area antenna 23.1984 22.2 12 11.77 3.84 3.58 3.22 3.27 efficiency antenna 0.5997264 0.6 0.586871 0.55 0.6324784 0.6 0.6 0.6 Noise Factor (dB) 4.5 4.5 2 2 5 5 4.8 4.8 26 Pulse frequency (Hz) 1464-1640 1146 3000 3915 3000-6500 4076 5336 (MPFR) Gain antenna dB/dBi 35 34.8 44.5 44.14 45 44.5 44.1 Repeat Cycle days 17 - 25 - 11 Period (min) 101 100.55 95.49 95.35 94.91667 97.06667 Orbit Inclination (de- 108 98.53 97.55 97.5387 97.44 97.46 97.86 grees) Atmospheric/Ionospheric 0.0099/3 0.3577 2.68 0 0 Loss (dB) Radar Loss (dB) 2.5 2.5 2.5 4.5 4.5 System Bandwidth (MHz) 19 83.3 300 Radar Transmitter Aver- 55 30.87(37.05) 200 9.16(10.99) +/- 400 417.03(500.44) 168.49 158.11(189.73) age Power (W) Peak Power (W) 1000 2880 2260 581 Duty Cycle(%) 0.055 7-8 18% (trans- 29 mit) SNR(dB) 9? Noise Equivalent Power design: - -21 -17 -17 -22: typical/- -21 -15 -15 (dB) 25/obtained: 19: worst case -21 Swath(km) 100 524.6 25/30 102 15(30) 56.4 28.55 68.9 Polarization HH - single/dual single/dual Pulse duration (mi- 33.4 872.8 20/10 263.1 245.3 187.4 cros)/maximum pulse duration Mass (kg) 147 antenna pan- 394 els: 870, 950 total Power Consumption (W) 216 3100/3500 4500 Reference Jordan Misra et al. Buckreuss ? (1980); (2002); Tyagi et al.(2009, Thompson (2007) 2003); Graf- and A.(1976) mueller et al. (2005)

27 Table 5.1: Real and theoretical values obtained with derived MATLAB code for various SAR-instruments 5.2.2 Feasibility for small satellites

Based on the spatial, temporal and frequency requirements for each of the disasters the required area, power and the number of satellites needed are calculated with the MATLAB script, the results can be found in tables 5.2,5.3 and 5.4.

SAR parameters for disas- Earthquake ter requirements Band L C X Parameters KP PC KP PC KP PC Along-track resolution 4 4 4 4 4 4 (m) Orbit height (km) 512 512 512 512 512 512 radar frequency (GHz) 1.5 1.5 6 6 10 10 Incidence angle(degree) 38.6 38.6 38.6 38.6 38.6 38.6 Area antenna(m2) 24.3 24.3 6.1 6.1 3.7 3.7 eﬃciency antenna 0.6 0.6 0.6 0.6 0.6 0.6 Noise Factor (dB) 3 3 3 3 3 3 Pulse frequency (Hz) 2927 2927 2927 2927 2927 2927 (MPFR) Gain antenna dB/dBi 36.6 36.6 42.6 42.6 44.9 44.9 Repeat cycle days 206 206 206 206 206 206 Period (min) 94.86 94.86 94.86 94.86 94.86 94.86 Orbit Inclination (de- 97.44 97.44 97.44 97.44 97.44 97.44 grees) Atmospheric/Ionospheric 0.012/3 0.012/3 0.41 0.41 3.1 3.1 Loss (dB) Radar Loss (dB) 2.5 2.5 2.5 2.5 2.5 2.5 System Bandwidth 37.5 37.5 37.5 37.5 37.5 37.5 (MHz) Average Power (W ) 33.4/ 33.4/ 73.4/ 73.4/ 225.8/ 225.8/ (Radar Transmitter 40.1 40.1 88.1 88.1 271.0 271.0 average power) Noise Equivalent Power -20 -20 -20 -20 -20 -20 (dB) Swath(km) 53.8 53.8 53.8 53.8 53.8 53.8 Pulse duration 342 342 342 342 342 342 (µsec)/max pulse duration Minimum number of satel- 49 25 49 25 49 25 lites Number of satellites 98 29 98 29 98 29 sun synchronous one or more orbit planes Revisiting time 12 hr 1 day 12 hr 1 day 12 hr 1 day

Table 5.2: Results of the required antenna area, average power and required number of satellites for detecting and monitoring earthquakes

As can clearly be seen, an SAR instrument in the L-band monitoring earthquakes, landslides and ﬂooding is not feasible on a small satellite as the required area is exceeding 20 square meters. Although the minimum area for a SAR instrument detecting drought would also require more than 20 square meters, Freeman et al.(2000) state that it is possible to reduce the minimum area, resulting however in either a reduced swath width or coarser resolution. The last column in table 5.4 shows for the same requirements a smaller area and smaller swath width, normally this would result in more satellites to meet the temporal resolution. However as the temporal requirement

28 SAR parameters for disas- Landslide ter requirements Band L C Parameters KP AC PC KP AC PC Along-track resolution 3 10 10 3 10 10 (m) Orbit height (km) 512 512 512 512 512 512 radar frequency (GHz) 1.5 1.5 1.5 6 6 6 Incidence angle(degree) 44.1 45.5 45.5 44.1 45.5 45.5 Area antenna(m2) 31.8 34.1 34.1 7.9 8.5 8.5 eﬃciency antenna 0.6 0.6 0.6 0.6 0.6 0.6 Noise Factor (dB) 3 3 3 3 3 3 Pulse frequency (Hz) 3806 1171 1171 3806 1171 1171 (MPFR) Gain antenna dB/dBi 37.7 38.1 38.1 43.8 44.1 44.1 Repeat cycle days 206 206 206 206 206 206 Period (min) 94.86 94.86 94.86 94.86 94.86 94.86 Orbit Inclination (de- 97.44 97.44 97.44 97.44 97.44 97.44 grees) Atmospheric/Ionospheric 0.013/3 0.013/3 0.013/3 0.45/3 0.46/3 0.46/3 Loss (dB) Radar Loss (dB) 2.5 2.5 2.5 2.5 2.5 2.5 System Bandwidth 50.0 15.0 15.0 50.0 15.0 15.0 (MHz) Average Power (W ) 32.6/ 9.1/ 10.9 9.1/ 10.9 72.2/ 20.1/ 20.1/ (Radar Transmitter 39.1 86.6 24.1 24.1 average power) Noise Equivalent Power -20 -20 -20 -20 -20 -20 (dB) Swath(km) 36.2 117.6 117.6 36.2 117.6 117.6 Pulse duration 263 854 854 263 854 854 (µsec)/max pulse duration Minimum number of satel- 19 268 6 19 268 6 lites Number of satellites 30 552 10 30 552 10 sun synchronous one or more orbit planes Revisiting time 2 days 1 hr 2 days 2 days 1 hr 2 days

Table 5.3: Results of the required antenna area, average power and required number of satellites for detecting and monitoring landslides

29 SAR parameters for disas- Flooding Drought ter requirements Band L C L Parameters AC PC AC PC AC AC Along-track resolution 30 2 30 2 500 500 (m) Orbit height (km) 512 512 512 512 512 512 radar frequency (GHz) 1.5 1.5 6 6 1.5 1.5 Incidence angle(degree) 38.56 33.06 38.56 33.06 38.59 38.56 Area antenna(m2) 24.3 18.7 6.1 4.7 24.3 3.5 eﬃciency antenna 0.6 0.6 0.6 0.6 0.6 0.6 Noise Factor (dB) 3 3 3 3 3 3 Pulse frequency (Hz) 586 5855 586 5855 586 586 (MPFR) Gain antenna dB/dBi 36.6 35.5 42.6 41.5 36.6 28.2 Repeat cycle days 206 206 206 206 206 206 Period (min) 94.86 94.86 94.86 94.86 94.86 94.86 Orbit Inclination (de- 97.44 97.44 97.44 97.44 97.44 97.44 grees) Atmospheric/Ionospheric 0.012/3 0.011/3 0.41 0.38 0.012/3 0.012/3 Loss (dB) Radar Loss (dB) 2.5 2.5 2.5 2.5 2.5 2.5 System Bandwidth 5.0 75.0 5.0 75.0 0.3 0.3 (MHz) Average Power (W ) 4.5/ 5.3 94.9/ 9.8/ 11.8 205.7/ 0.27/ 12.9/ (Radar Transmitter 113.1 246.9 0.32 15.5 average power) Noise Equivalent Power -20 -20 -20 -20 -20 -20 (dB) Swath(km) 403.6 30.7 403.6 30.7 6726.7 968.1 Pulse duration 2600 171 2600 171 1700 1700 (µsec)/MPRF Minimum number of satel- 7 22 7 22 1 1 lites Number of satellites 14 35 14 35 1 1 sun synchronous one or more orbit planes Revisiting time 12 hours 2 days 12 hours 2 days 6 days 6 days

Table 5.4: Results of the required antenna area, average power and required number of satellites for detecting and monitoring Flooding and Drought

30 for drought is not as severe as for other disasters, one satellite could still meet the requirements. Despite that many modern SAR instruments have multiple looking features and therefore could increase the possible swath, it is assumed that even if this is the case the SAR instruments in the other bands require too many satellites (>25) to meet the mass and budget requirements. The C-band SAR instruments monitoring landslides during the PC-fase and ﬂooding during the AC-phase, who require less satellites, might be a possibility as a feasible application meeting the set requirements. As the MATLAB script did not determine the weight or cost of the SAR systems, an overview of those parameters is given for some recent and future SAR missions in table 5.5.

31 Instrument PALSAR-2 SAR-C SAR (RadarSat-1) TecSAR/ EL/ M- SAR Taiwan NovaSAR 2070 SAR Weight(kg) 658 880 705 100 130 Peak Power(W ) 5000 4400 1650 1600 W (bus give 750 600 W 1800 W ) Area (m2) 28.71 12.55 20.55 solid reﬂector with length: min 6 m 3 horn, 3.5 m diameter Resolution(m) 1 to 100 4 to 80 3-100 1-8 5 6-30 coverage/cycle global: minimum 2 Global coverage: 5 Near-global coverage: 3-4 days weeks (gapless cover days (duty cycle 1 week, longer peri- in 42 or 56 days by 70%), other opera- ods for other modes; stripmap mode) tion modes up to 3 shorter on selected months operation Swath(km) 25-350 80-400 20-500 25 20-150 Band LCCXCS satellite ALOS-2 Sentinel-1A RadarSat-2 TecSAR FOMOSAT SSTL NovaSAR-S years active 2014-2019 2014-2021 2007-current 2008-current - 2015-2022

32 cost sat xmillion 384 487.7 200 34.8 USD Ref Kramer(2014e) Kramer(2014f) Kramer(2014g) Kramer(2014a) Kramer(2014h)

Table 5.5: Overview of weight, resolution, swath, antenna area of different SAR satellites and the cost of the satellite the instrument is on board The last two columns show the specifics of two proposed SAR instruments for smal(ler) satellites. From the table it can be concluded that in general SAR instruments would be too heavy or costly for a disaster monitor small satellite constellation. However as only one satellite is required to monitor drought the recent developments as described by Saito et al.(2013),Yaung et al.(2014) and Bird et al.(2013) looks promising to fly a SAR instrument on a small satellite. But it should be noted, that at the moment there is not a developed or proposed L-band SAR instrument for a small satellite. To conclude, SAR instruments are not feasible on board of a small satellite monitoring and detecting disasters except there might be a possible application for detecting drought in the L-band, an in depth analysis and cost estimation for this application are necessary to ascertain this.

33 Chapter 6

Feasibility of passive optical instruments for the a small satellite constellation

In this chapter an answer will be given about the possibility of a passive optical instrument on board of small satellites for disaster management. The first approach to answer was to develop again an algorithm in MATLAB that could be used to calculate the design parameters for a passive optical sensor. The algorithm was based on a method described in chapter 9 of Wertz and Larson(1999). Although the entire algorithm has been developed as can be seen in Appendix B and some initial succesfull tests were performed, it was deemed that another approach would better in answering the question. Instead of checking the feasibility of the optical sensor on board a small satellite that is meeting the required performances based on the sensors size, weight and as possible costs, it is first checked if optical sensors could actually meet the set spatial and temporal requirements set for the particular disasters India is facing. This is done by exploring the effect of clouds on the visibility of the optical sensor and the restrictions caused by the derivations of indexes, as is presented in this chapter.

6.1 Visibility constraints caused by clouds

The existence of cloud cover appears as the single most important impediment to capture disasters, especially the progress of floods and landslides in bad weather condition (Rango and Salomonson(1974); Imhoff et al.(1987); Joyce et al.(2009)). Especially considering that most floods and landslides in India will occur during the monsoon or under bad weather conditions (Srivastava(2011)), it is important to make an estimation of the chance that the instrument has a unobstructed field-of-view or in other words the availability of the instrument. Data about the distribution of cloud cover is surprisingly difficult to find publicly. Some publications have been successfully answering this questions by looking at the observed cloud cover from satellites as can been found in Ogunbadewa(2012); Asner(2001). However detailed global data of the distribution of the cloud cover derived from satellite observations seems to be missing. After correspondence with William B. Gail, CTO of Global Weather Corporation and President of the American Meteorological Society, the two publicly available initiatives that could be used to estimate the cloud cover are Climatic Atlas of Clouds Over Land and Ocean(CACOLO) (Eart- man et al.(2014)) and the International Satellite Cloud Climatology Project (ISC(2014)). The CACOLO land maps are based on analyses of 18 million visual cloud observations made at 5388 weather stations on continents and islands over a 26-year period from 1971-1996. The ISCCP was established in 1982 as part of the World Climate Research Programme (WCRP) to collect and analyze satellite radiance measurements to infer the global distribution of clouds, their properties, and their diurnal, seasonal, and interannual variations. It has climatology monthly and seasonal mean maps available based on a period from 1983 till 2009. An overview of the monthly data of both sources can be found in 6.1. As can be concluded from the table monitoring and detecting any disaster with the requirement of updates of at least every two days is quite difficult during the monsoon. Only the monitoring of drought optically might be less constrained as drought is occurring normally not in the monsoon. However as will explained in the next section drought is optical monitored based on indexes having its own limitations.

34 Month CACOLO CACOLO CACOLO CACOLO ISCCP (South) (North) Day Day (South) (North) January 40.0 18.8 49.0 21.3 20 February 35.0 17.6 38.2 20.9 25 March 34.3 19.8 37.0 21.6 30 April 49.3 27.3 51.0 27.6 45 May 60.7 35.4 61.0 34.8 60 June 75.2 64.9 76.2 65.3 70 July 77.3 80.6 78.3 81.8 80 August 74.7 80.8 75.5 82.0 80 September 68.2 61.9 69.3 64.8 70 October 65.3 37.0 67.5 39.9 60 November 60.5 25.9 62.2 28.3 40 December 49.7 21.0 53.7 24.3 30

Table 6.1: Monthly average cloud amount in percentage estimated for India from CACOLO (day and average) and ISCCP(average)

6.2 Constraints imposed by the use of indexes

It should be noted that the derivations of indexes takes time as data needs to be validated and is often only valid for one (type) of instrument. The NDVI used for monitoring droughts and ﬂooding could be derived and used as there have been multiple missions with the AVHRR instrument on board (Products and Division(2013)). For a short term mission (below ﬁve years), as most small satellite missions are (Sandau et al.(2010)), with an uncertain following up, indexes are less suitable to be used to determine the occurrence of disasters.

35 Chapter 7

Conclusions and recommendations

This report is answering the question if a small satellite constellation dedicated to disaster management is a feasible option and worth to pursue. It has been shown that disasters in India cause more than 1,400 million victims and cost the society more than 50,000 million USD in the last thirty years. Especially the natural disasters have the greatest impact. Furthermore disasters happen almost in all parts of India and the frequence of some of these disasters will only increase due to climate changes. It is therefore clear that effective disaster management is necessary to reduce the impact of disasters and help the society recover faster. An important tool for disaster management is remote sensing as satellites are able to view remote areas as well as to screen large areas at the same time at regular time intervals. It has been indicated that the greatest impact in India can be made in the remote sensing of earthquakes, landslides, flooding and drought. Based on the Indian space budget, the cost of existing disaster monitor constellations and the economical damage caused by the disasters, it is derived that the cost for a small satellite constellation dedicated to landslides could not exceed 1-3 million USD for a five year mission. The maximum cost for a constellation dedicated to earthquakes, flood and drought is estimated at 30 million USD to be cost effective. This results in a mass constraint of 500 kg and a volume constrained of 5 m3 combined for all satellites together. Depending on the weight of the satellite the maximum average power varies between 50-70 W for a 100 kg and 400 W for a 500 kg satellite. As the cost of a satellite constellation is also greatly depended on the number of satellites in the constellation, a proposal for a small satellite constellation has been made. To save mass in the form of propellant and having beneficial illumination, a sun-synchronous constellation with as less orbit planes as possible is proposed, resulting in one orbit plane for a revisiting time of one day or more. This constellation is modeled in MATLAB based on the orbit height and required revisiting interval to determine the required number of satellites. Lastly the feasibility of two types of instruments with proven capabilities, the microwave and optical instruments, on board of small satellites is critically researched. Active microwave instruments, more specific SAR, are proven to be useful. However due to the derived requirements for many of the different phases of the disasters the instruments require a too large antenna panel or too many satellites. Only the C-band SAR instruments monitoring landslides during the PC-fase and flooding during the AC-phase, who require less satellites, might be a possibility as a feasible application meeting the set requirements. After analyzing recent and future SAR missions it became evident that these two instruments would not meet the mass and cost constraints with the number of satellites needed in the constellation. Optical instruments are not suitable for a mission dedicated to disaster management due to the cloud cover visibility constraint and limitations set by the usage of indexes. To conclude a satellite constellation dedicated to disaster management might be technical feasible in meeting the temporal and spatial requirements if SAR instruments would be used as payload. It would be worth to pursue a disaster monitor mission as the human and economical damage caused by the different disasters is substantial. However with the constraints set for small satellites in this report, a small satellite constellation dedicated to disaster management for the Indian subcontinent is not feasible and it is therefore not recommended to pursue this. Although it should be noted that this report saw an opportunity for a mission dedicated to drought monitoring for a single small satellite if a L-band SAR instrument would be developed

36 compatible for small(er) satellites. It is recommend that an in-depth study is conducted into this possibility. Other recommendations are to preform proper research into the effectiveness of satellite missions in reducing the impact of disasters as research in this field in severely lacking. Expensive missions are launched while the return value is unclear. Furthermore improvements can be made in making up-to-date cloud-cover data (publicly) available to determine the imposed visibility for optical sensors. This report could be improved by validating and refining the SAR sizing method further by comparing the theoretical values produced by the tool with various existing SAR missions. Also the losses, such as the atmospheric losses and radar losses can be modeled more precisely to improve the results of the SAR sizing algorithm. Furthermore it has not been researched what value of the noise-equivalent sigma-zero or in other words the sensitivity would be necessary to perform the mission, an assumption based on existing missions has been taken. It is recommended to validate this value. Another point to address is that during the determination of the number of satellites in the constellation, a fixed line of sight is assumed, meaning that the satellite is not agile. This results in a limitation in coverage. If the satellite would be able to change the looking angle, the coverage is increased and less satellites could be necessary to observe India. However this would mean a higher complexity of the satellite as well as a possible higher mass due to the required attitude determination and control of the satellite and/or instrument. A study focusing on agile small satellites would be a good addition to the study presented in this report. This report could be further completed by conducting a cost estimation for the satellites. Lastly this report focused only on coverage of India, it can be interesting to investigate the opportunities of cooperation with other countries in developing a (SAR) disaster monitor constellation or the opportunity to sell disaster data to other countries. This would increase the mission budget and opens up new possibilities to deploy small or smaller satellites for disaster management.

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43 Appendix A

1 %% SAR sizing tool 2 % This tool can be used to estimate the area required as well as the power 3 % necessary. 4 5 %% Start up 6 close all; 7 clear all; 8 clc 9 10 %% Prompt the user to give the required input values 11 prompt ='What is the required along track resolution in meters?'; 12 alresolution = input(prompt); 13 prompt ='What is the required revisiting time in days?'; 14 revtime = input(prompt); 15 prompt ='What is the orbit height in kilometers?'; 16 orbitheight = input(prompt)*1000; 17 prompt ='What is the radar frequency in GHz?'; 18 f_radar = input(prompt)*10^9; 19 prompt ='What is the maximum incidence angle in degree?'; 20 betta = 90 - input(prompt);% betta is the grazing angle, which is the complementary 21 % angle to the incidence angle 22 prompt ='What is the minimum incidence angle in degree?'; 23 betta_min = 90-input(prompt); 24 25 %% Set the constant parameters 26 GM = 398600.4418e9;%m^3/s^2; Earths gravitational constant multiplied with mass earth 27 R = 6378136.49;%m; Earth's equatorial radius 28 c = 299792458;%m/s; speed of light ina vacuum 29 k = 1.5;% Antenna beam roll-off at least larger than1.5 30 k_B = 1.380658e-23;%J/K; Boltzmann's constant 31 T = 290;%K; Nominal scene noise temperature 32 eff_antenna = 0.6;% Aperture efficiency of the antenna 33 dc = 1;% Antenna area design parameter 34 a_wr = 1.2;% range impulse responde broading factor due to data weighting 35 sigma_0ref = -20;% dB; reflectivity at nominal reference frequency when SNR=1 36 L_radar = 2.5;% dB; microwave transmission loss factor miscellaneous: radar losses 37 F_N = 3;% dB; System Noise Factor/figure 38 L_ion = 0;% Loss caused by the ionosphere, only relevant forL-band 39 revisiting_time = 0;% changes if the revisting time is smaller than one day 40 cloud_height = 5000;% path integrated attenuation fora propagation scenario 41 % witha raincell height 42 omega = 0.9856;% deg/day; rates of change of right ascension of the ascending node 43 J2 = 0.00108263;% no unit; dimensionless geopotential coefficient 44 45 %% Convert parameters 46 labda = c/f_radar;% Calculating the wavelength of the radar 47 48 %% Calculate the circular velocity of the spacecraft corresponding to the orbit 49 r = orbitheight+R; 50 Vcir = (GM/r)^(1/2);% velocity spacecraft inm/s, assumption spherical earth 51 52 %% Calculate the necessary range bandwidth 53 Bandwidth = c/(2*alresolution);% Hz, assuming that the along track and 54 % crosstrack resolution are the same 55 56 %% Step 1: Calculating the area for the SAR panel and maximum swath 57 % Calculate the maximum length of the antenna from the along track resolution 58 La_max = alresolution*2; 59 La = La_max; 60

44 61 % Pulses in the air 62 % Calculate the minimum PRF due to doppler considerations and thereby the maximum Tp 63 minPRF = (2*k*Vcir)/La; 64 Tp = 1/minPRF; 65 66 % Calculte the minimum index np and round up to integer 67 np = ((2/c)*orbitheight)/Tp; 68 np = round(np+0.49999999);% rounding up to nearest integer 69 70 % Calculate the corresponding grazing angle(complementary angle to incidence angle) 71 mp = np;% Satisfying condition mp>=np 72 graz_angle = asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 73 ((orbitheight*((mp+0.5)/np))/(2*R))); 74 graz_angle_deg = rad2deg(asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 75 ((orbitheight*((mp+0.5)/np))/(2*R)))); 76 while(graz_angle_deg < betta) 77 np = np + 1; 78 mp = mp + 1; 79 graz_angle=asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 80 ((orbitheight*((mp+0.5)/np))/(2*R))); 81 graz_angle_deg = rad2deg(graz_angle); 82 while (graz_angle_deg > betta_min) 83 mp = mp+1; 84 graz_angle=asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 85 ((orbitheight*((mp+0.5)/np))/(2*R))); 86 graz_angle_deg = rad2deg(graz_angle); 87 end 88 end 89 90 while (graz_angle_deg > betta_min) 91 mp = mp+1; 92 graz_angle=asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 93 ((orbitheight*((mp+0.5)/np))/(2*R))); 94 graz_angle_deg = rad2deg(graz_angle); 95 while(graz_angle_deg < betta) 96 np = np + 1; 97 graz_angle=asin(np/(mp+0.5)*(1+(orbitheight/(2*R)))- 98 ((orbitheight*((mp+0.5)/np))/(2*R))); 99 graz_angle_deg = rad2deg(graz_angle); 100 end 101 end 102 103 % Calculate the distance to the targetR(range vector from target to 104 % antenna), new Tpmaximum and minimum PRF, and the incidence angle 105 Range = orbitheight * (mp+0.5)/np;%m 106 Tpmax = (2/c)*orbitheight/np;% seconds 107 min_PRF = 1/Tpmax;% Hz 108 inc_angle = 0.5*pi - graz_angle;% rad 109 inc_angle_deg = rad2deg(inc_angle);% deg 110 111 % Calculating the widest possible swath in ground range 112 swath = alresolution * (c/((4.7*Vcir)*cos(graz_angle))); 113 114 % Calculating the minimum area or area for optimal swath and resolution and 115 % the design antenna 116 A_antennamin=4*Range*Vcir*(labda/c)*tan(inc_angle); 117 A_antenna = dc* A_antennamin; 118 A_antennamin_skolnik = 9.4*Range*Vcir*(labda/c)*tan(inc_angle);% Unambigious range 119 A_antenna_skolnik = dc*A_antennamin_skolnik; 120 121 %% Step 2: Calculating the required average power 122 123 % Antenna(monostatic) 124 % Effective area

45 125 A_e = eff_antenna * A_antenna_skolnik; 126 127 % Transmitter Antenna Gain factor G_A, no unit 128 G_A = (4*pi*A_e)/(labda^2); 129 130 G_AdB = pow2db(G_A); 131 132 % Atmospheric losses 133 % determining the spefcic attentuation witha rain rate 12.5 mm/hr 134 if f_radar > 1e9 && f_radar< 2e9%L-band 135 L_ion = 3; 136 alpha = 0.0009;% dB/km; specific attenuation 137 elseif f_radar>= 2e9 && f_radar<4e9%S-band 138 alpha = 0.005;% dB/km; two-way atmospheric loss rate 139 elseif f_radar>= 4e9 && f_radar<8e9%C-band,6 GHZ 140 alpha = 0.032;% dB/km; two-way atmospheric loss rate 141 elseif f_radar>= 8e9 && f_radar<12e9%X-band, 10 GHz 142 alpha = 0.24;% dB/km; two-way atmospheric loss rate 143 else 144 alpha = 2; 145 end 146 147 % Calculating all the losses 148 two_way_atm = 2*cloud_height/cos(inc_angle); 149 L_atmos = 10^((alpha*(two_way_atm/1000))/10); 150 L_atmos_dB = alpha*(two_way_atm/1000);% dB; atmospheric losses 151 152 % determining the total loss in Decibel 153 Loss = L_radar + L_atmos_dB+L_ion; 154 155 % Determine the system noise temperature in Kelvin 156 Ts = T*(10^(F_N/10))-T; 157 158 % Convert to parameters from decibel to non-decibel values 159 Loss_pow = db2pow(Loss); 160 F_Npow = db2pow(F_N); 161 sigma_0refpow = db2pow(sigma_0ref); 162 163 % Skolnik average power in Watt 164 P_avg_skolnik = (8*pi*Range^3*labda*k_B*T*F_Npow*Loss_pow*Vcir)/ 165 (A_antenna_skolnik^2*eff_antenna^2*sigma_0refpow*alresolution); 166 167 % Average power with the factor a_wr, the azimuth impulse response broading factor 168 P_avg = (8*pi*Range^3*labda*k_B*T*F_Npow*Loss_pow*Vcir*a_wr)/ 169 (A_antenna_skolnik^2*eff_antenna^2*sigma_0refpow*alresolution); 170 171 %% Calculating the sun synchronous orbit parameters 172 173 % Calculate the required cycles needed to cover the entire earth/ equator. 174 % assumption that India is close enough to the equator. 175 cycles = 2*pi*R/swath; 176 177 % Calcuate the period and inclination to the corresponding height 178 period = 2*pi*sqrt(r^3/GM);% seconds 179 period_min = period/60;% Period in mins 180 mean_motion = (GM/r^3)^(1/2);% rad/s 181 omegarad = deg2rad(omega/(24*3600));% change omega in deg/day to rad/s 182 incl = rad2deg(acos(omegarad/(-1.5*mean_motion*J2*(R/r)^2)));% deg 183 184 % Calculate the revolutions per day 185 rev_day = 86400/period; 186 [rev_day_num ,rev_day_den] = rat(rev_day); 187 total_rev = rev_day_den*rev_day; 188

46 189 % Calculate the change in longitude between consecutive nodal crossings 190 delta_L = 360 * rev_day_den/total_rev;% deg 191 192 % Calculate the difference between two adjacent nodes 193 delta_l = 360/total_rev;% deg 194 195 % Calculate the rotation speed of the earth in deg/seconds 196 rot_earth = 360/ (24*60*60);% deg/seconds 197 198 % Calculate the minimum amount of satellites in sunsynchronous, but 199 % different local times and thus different orbit planes; circumference of earth 200 %divided by swathwidth, revolution of the day and the required revisiting time, 201 % as the satellites crosses two times the equator factor2 202 sat_tot_min = 2*pi*R/(2*(swath/cos(deg2rad(incl-90)))*rev_day*revtime); 203 sat_tot_min = round(sat_tot_min+0.4999999); 204 205 % If the required revisiting time is smaller than one day, reset the 206 % revisiting time and note it 207 if revtime<1 208 revisiting_time = revtime; 209 revtime=1; 210 end 211 212 %% Calculate the minimum satellites ina sunsynchronous orbit, one orbit 213 % plane, only looking at ascending nodes, only valid if revisiting time is 214 % less than one day 215 216 if revisiting_time>0 217 218 % Calculating the angle of the first ascending node 219 i = 2; 220 angle(1:(revtime+1)) = zeros; 221 while(i

47 253 time_sats = angle_sat/rot_earth; 254 255 256 % Calculate the velocity of the satellite in deg/seconds 257 rot_satellite = 360/period;%deg/s 258 259 % Calculate the angular spacing of satellites in orbit 260 spacing_sat = rot_satellite*time_sats; 261 262 % If the revisiting time is lesser than one day 263 if revisiting_time>0 264 sat_tot_sun2= sat_tot_sun/revisiting_time; 265 end 266 267 end 268 %% Calculate the minimum satellites ina sunsynchronous orbit, one orbit 269 % plane, both looking at ascending and descending nodes 270 271 if revisiting_time ==0 272 % Creating loop to calculate all the angles 273 i = 1; 274 surplus = 0; 275 p = 0; 276 z = round(rev_day*revtime); 277 angles_asc(1:(2*z))=zeros; 278 while (i<(rev_day*revtime)) 279 angles_asc(i) = delta_L*p+surplus; 280 if angles_asc(i)>360 281 surplus = angles_asc(i)-360; 282 p = 0; 283 angles_asc(i) = surplus; 284 end 285 angles_asc(i+z) = angles_asc(i)+180; 286 if angles_asc(i+z) > 360 287 angles_asc(i+z) = angles_asc(i+z)-360; 288 end 289 i = i+1; 290 p = p+1; 291 end 292 293 294 % Sort the angles of the first ascending node from lowest to biggest 295 angle_asc_sort=sort(angles_asc); 296 297 % Calculate the difference between the ascending nodes 298 angle_asc_dif(1:(2*z-1))=zeros; 299 for n = 1:(2*z-1) 300 angle_asc_dif(n) = angle_asc_sort(n+1)-angle_asc_sort(n); 301 end 302 303 % Determine the maximum difference between the ascending nodes 304 angle_asc_dif_max = max(angle_asc_dif); 305 306 %Determine the corresponding arc spanning on the earth surface 307 arc_asc = angle_asc_dif_max/360*(2*pi*R); 308 309 % Determine the amount of satellites necessary to cover this area 310 sat_tot_sun_asc = arc_asc/(swath/cos(deg2rad(incl-90))); 311 sat_tot_sun_asc = round(sat_tot_sun_asc+0.4999999); 312 313 % Calculate the spacing between the satellites where they are crossing the 314 % equator 315 angle_sat_asc = angle_asc_dif_max/(sat_tot_sun_asc+1); 316

48 317 % Calculate the time between the ascending node crossings 318 time_sats_asc = angle_sat_asc/rot_earth; 319 320 321 % Calculate the velocity of the satellite in deg/seconds 322 rot_satellite = 360/period;%deg/s 323 324 % Calculate the angular spacing of satellites in orbit 325 spacing_sat_asc = rot_satellite*time_sats_asc; 326 327 end

49 Appendix B

1 %% Calculation design parameters fora passive optical sensor 2 % Visible and infrared 3 % Based on SMAD Table 9-15 4 5 %% Step0 Start up 6 close all; 7 clear all; 8 clc 9 10 % Define constants 11 GM = 398600.4418e9;%m^3/s^2; Earths gravitational 12 % constant multiplied with mass earth 13 R = 6378136.49;%m; Earth's 14 c = 299792458;%m/s; speed of light ina vacuum 15 T_day = 24*60*60;%s; time duration1 day 16 h = 6.6260755e-34;%W *s^2, Planck's constant 17 k = 1.380658e-23;%W *s/K, Boltzmann's constant 18 19 %% Step1 Define orbit parameters 20 21 % Prompt the user to give the required input values 22 prompt ='What is the required along track resolution in meters?'; 23 alresolution = input(prompt); 24 prompt ='What is the required revisiting time in days?'; 25 revtime = input(prompt); 26 prompt ='What is the orbit height in kilometers?'; 27 orbitheight = input(prompt)*1000; 28 prompt ='What is the sensorsensor size in cross direction(no of pixels)?'; 29 z_pixels_cross = input(prompt); 30 prompt ='What is number of bits used to encode each pixels?'; 31 bits = input(prompt); 32 prompt ='What is the operating wavelength(red:0.7e-6, blue:0.45e-6, 33 green:0.55e-6, NIR:1.3 or1.6 or2.4, Mid Infrared:3.5 or3.8?'; 34 wavelength = input(prompt); 35 36 37 % Compute orbit periodP 38 r = orbitheight+R;%m; Obitheight plus radius of earth 39 P = 2*pi*sqrt(r^3/GM);% seconds; orbit period 40 41 % Compute ground track velocity V_g 42 V_g = 2*pi*R/P;%m/s 43 44 % Compute nodal shift delta_L 45 delta_L = P/T_day*360;% deg 46 47 %% Step2 Define Sensor viewing parameters 48 49 % Comptute angular radius of the Earth rho 50 rho = asin(R/r);% radians 51 rho_deg = rad2deg(rho);% degrees 52 53 % Compute the maximum distance to the horizon D_max 54 D_max = (r^2-R^2)^0.5;% meters 55 56 % Compute the maximum Earth Central angle lamda_0_deg 57 lamda_0_deg = 90-rho_deg;% deg 58 59 % Compute the sensor look angle (=nadir angle) eta_deg 60 % Createa loop to estimate eta

50 61 rho_deg_approx = round(rho_deg-0.49999); 62 z(1:rho_deg_approx,1) = zeros; 63 for i = 1:rho_deg_approx 64 i_rad = deg2rad(i); 65 z(i) = 2*i_rad/((alresolution*cos(0.5*pi-acos(sin(i_rad)/sin(rho))))/ 66 (R*(sin(0.5*pi-i_rad-acos(sin(i_rad)/sin(rho))))/sin(i_rad))); 67 end 68 69 % Find the closest values ofz(i) to the value of Zmax 70 Z_max = z_pixels_cross;% Max pixels of sensor in cross direction 71 val = Z_max;% value to find 72 tmp = abs(z-val); 73 [idx idx] = min(tmp);% index of closest value 74 closest = z(idx);% closest value 75 76 % Find the value of eta more precise 77 z = 0; 78 z(1:10, 1:2) = zeros; 79 for i = 0:0.1:1 80 g = deg2rad(idx - 0.5 + i); 81 b = round(i*10+1); 82 z(b,1) = idx - 0.5 + i; 83 z(b,2) = 2*g/((alresolution*cos(0.5*pi-acos(sin(g)/sin(rho))))/ 84 (R*(sin(0.5*pi-g-acos(sin(g)/sin(rho))))/sin(g))); 85 end 86 87 tmp = abs(z(:,2)-val); 88 [idx idx] = min(tmp);% index of closest value 89 closest = z(idx,2);% closest value 90 eta_temp = z(idx,1);% Angle corresponding to eta 91 92 z = 0; 93 z(1:10, 1) = zeros; 94 for i = 0:0.01:1 95 g = deg2rad(eta_temp - 0.05 + i); 96 b = round(i*100+1); 97 z(b,1) = eta_temp - 0.05 + i; 98 z(b,2) = 2*g/((alresolution*cos(0.5*pi-acos(sin(g)/sin(rho))))/ 99 (R*(sin(0.5*pi-g-acos(sin(g)/sin(rho))))/sin(g))); 100 end 101 102 tmp = abs(z(:,2)-val); 103 [idx idx] = min(tmp);% index of closest value 104 closest = z(idx,2);% closest value 105 eta_temp = z(idx,1);% Angle corresponding to eta 106 107 eta_deg = eta_temp;% nadir angle 108 109 % Compute the elevation angle epsilon 110 epsilon = acos(sin(deg2rad(eta_deg))/sin(rho));% rad 111 epsilon_deg = rad2deg(epsilon);% deg 112 113 % Compute Earth Central angle, lamda 114 lamda_deg = 90-epsilon_deg-eta_deg;% deg 115 116 % Compute the incidence angleIA 117 IA_deg = 90 - epsilon_deg;% deg 118 119 % Compute the slant range R_s 120 R_s = R*(sin(deg2rad(lamda_deg))/sin(deg2rad(eta_deg)));% meters 121 122 % Find swath width 123 swath_deg = 2*lamda_deg; 124 swath = 2*pi*R*(swath_deg/360);

51 125 126 %% Step3 Define pixal parameters and data rate 127 128 % Find maximum cross-track pixel resolution X_max at ECA_max 129 X_max = alresolution;% meters 130 131 % Specify the maximum along track ground sampling distace Y_max 132 Y_max = X_max* cos(deg2rad(IA_deg));% meters 133 134 % Determine instantaneous field of view IFOV 135 IFOV = Y_max/R_s*(180/pi);% deg 136 137 % Determine the cross-track ground pixel resolution,X, at nadir 138 X = IFOV * orbitheight*(pi/180);% meters 139 140 % Determine along-track pixel resolution,Y, at nadir 141 Y = IFOV * orbitheight*(pi/180);% meters 142 143 % Determine no of cross-track pixels Z_c 144 Z_c = 2*eta_deg/IFOV; 145 146 % Find the number of swaths recorded along-track in one sec, Z_a 147 Z_a = V_g*1/Y; 148 149 % Find the number of pixels recorded in one sec,Z 150 Z = Z_c * Z_a; 151 152 % Specify no of bits used to encode each pixel,B 153 B = bits; 154 155 % Compute data rateDR 156 DR = Z*B; 157 158 %% Step4 Define sensor integration parameters 159 % Specify no. of pixels for whiskbroom inst. N_m 160 N_m = 0.5 * Z_c; 161 162 % Find pixel integration period T_i 163 T_i = Y/V_g*N_m/Z_c; 164 165 % Find resulting pixel read-out frequency, F_p 166 F_p = 1/T_i; 167 168 169 %% Step5 Define sensor optics 170 % Specifiy width for square detetorsd 171 d = 30e-6;% Typical for available detectors 172 173 % Specify quality factor for imagingQ 174 Q = 1.1;%0.5

52 189 FOV = IFOV*N_m;% deg 190 191 % Determine the cut-off frequency F_c 192 F_c = D/labda*orbitheight;% line pairs/m 193 194 % Determine the cross-track Nyquist frequency F_nc 195 F_nc = 0.5*X;% line pairs/m 196 197 % Determine the along-track Nyquist frequency F_na 198 F_na = 1/2*Y; 199 200 % Compute relative Nyquist frequencies F_qc and F_qa 201 F_qc = F_nc/F_c; 202 F_qa = F_na/F_c; 203 204 %% Step6 Estimate Sensor Radiometry(for Nadir Viewing) 205 % and step7 finding the Noise-equivalent temperature difference 206 i = 1; 207 while i>=0% creatinga loop to determine the parameters 208 % witha temperature difference of1K 209 % Define equivalent blackbody temperature T_black 210 T_black = 290+i;%K, black body temperature of Earth 211 212 % Define the operating bandwidth delta_labda 213 delta_labda =1.9e-6 ; 214 labda_min = labda - delta_labda/2; 215 labda_max = labda + delta_labda/2; 216 217 % Planck's law 218 E_labda = (2*pi*h*c^2)/labda^5*(1/(exp(1)^((c*h)/(k*T_black*labda))-1)); 219 220 % Determine the blackbody spectral radiance L_labda 221 L_labda = E_labda/4*pi; 222 223 % Estimate up transmissivity tau(labda) of the atmosphere 224 if labda < 1e-6 225 tau_labda = 0.9;% estimation of the transmissivity 226 % of the visible wavelength 227 elseif labda >= 1e-6 && labda<2.6 228 tau_labda = 0.75;% estimation of the transmissivity 229 % for the optimal bands of NIR 230 else 231 tau_labda = 0.8;% estimation of the transmissivity 232 % for optimal bands for the MIR 233 end 234 235 % Compute the upwelling radiance L_upi 236 L_upi =@(labda)(2.*pi.*h.*c.^2)./labda.^5.*(1./(exp(1).^((c.*h)./ 237 (k.*T_black.*labda))-1))./(4.*pi) .*tau_labda; 238 239 % Compute integrated upwelling radiance L_int 240 L_int = integral(L_upi,labda_min,labda_max);%W/m^2/sr 241 242 243 % Compute radiated powerL froma ground pixel at nadir 244 L = L_int*X*Y;%W/sr 245 246 % Compute the input power P_in at sensor 247 P_in = L/h^2*(D/2)^2*pi;%W 248 249 % Define optical transmission factor tau_0 250 tau_0 = 0.75;% typical value for optical systems 251 252 % Find the input power P_D at the detector pixel

53 253 P_D = P_in*tau_0;%W 254 255 % Determine available energyE after integration time 256 E = P_D *T_i;% Ws 257 258 % Find the# of photons available N_p 259 N_p = E*labda/h*c; 260 261 % Define quantum efficiencyQE of detector at labda 262 QE = 0.5;% Typical physical property of detector material 263 264 % Compute number of available electrons and Determine# of noise electrons N_n 265 if i ==1 266 N_e_new= N_p*QE; 267 N_e = N_e_new; 268 N_n = sqrt(N_e_new);% Considers only shot noise 269 else 270 N_e=N_p*QE; 271 N_n = sqrt(N_e); 272 end 273 % Define# of read-out noise electrond N_r 274 N_r = 25;% Typical value 275 276 % Determine total# of noise electrons N_t 277 N_t = sqrt(N_n^2+N_r^2);% Assumes uncorrelated noise 278 279 % Find signal-to-noise ratio of the image SNR 280 SNR = N_e/N_t;% Assuming signal dominates background 281 282 % Determine sensor dynamic range,DR 283 DR = N_e/N_r;% With respect to cold space 284 285 i =i-1; 286 end 287 288 %% Step7. Find the Noise-Equivalent temperature difference 289 % Determine number of charge carriers for1K. temp change 290 delta_N = N_e_new-N_e; 291 292 % Compute noise equivalent temperature difference 293 NEDeltaT = N_e/delta_N;